Transit Use During and After the COVID-19 Pandemic: The “New Normal” for Public Transit Ridership
Bibliographic record
Abstract
The Covid-19 pandemic significantly impacted transit ridership across Canada. As the pandemic begins to subside, understanding the factors that influence peoples’ decisions to use transit (or not) is crucial for the recovery and long-term sustainability of public transit. Using data from the third wave of the Public Transit and Covid-19 survey in Canada, this study evaluates who returned to pre-pandemic transit use, the factors influencing the decision to ride transit, and peoples’ intentions for future transit use. The authors find that most transit riders perceive that the pandemic is over but its effects are here to stay, though they are split about whether the pandemic still affects their transit use. While some transit riders have gradually returned to pre-pandemic transit levels, a relatively small share of those who have not yet fully returned intend to and a significant proportion do not intend to fully return. About half of transit riders will return to transit at a lower usage level than before the pandemic, while about 10% do not intend to return at all. The results indicate that in the “new normal”, transit use will remain below pre-pandemic levels for those who rode transit before the pandemic. Factors such as car access are significantly related to the extent to which people have returned to transit, although this may be reflecting a shift away from transit rather than causing the shift. Factors such as easy access to transit stops, service frequency, and proximity to home and job locations influence current transit use.View the NCST Project Webpage
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".